Knowledge discovery for moderating collaborative projects

In today's global market environment, enterprises are increasingly turning towards collaboration in projects to leverage their resources, skills and expertise, and simultaneously address the challenges posed in diverse and competitive markets. Moderators, which are knowledge based systems have successfully been used to support collaborative teams by raising awareness of problems or conflicts. However, the functioning of a moderator is limited to the knowledge it has about the team members. Knowledge acquisition, learning and updating of knowledge are the major challenges for a Moderator's implementation. To address these challenges a Knowledge discOvery And daTa minINg inteGrated (KOATING) framework is presented for Moderators to enable them to continuously learn from the operational databases of the company and semi-automatically update the corresponding expert module. The architecture for the Universal Knowledge Moderator (UKM) shows how the existing moderators can be extended to support global manufacturing. A method for designing and developing the knowledge acquisition module of the Moderator for manual and semi-automatic update of knowledge is documented using the Unified Modelling Language (UML). UML has been used to explore the static structure and dynamic behaviour, and describe the system analysis, system design and system development aspects of the proposed KOATING framework. The proof of design has been presented using a case study for a collaborative project in the form of construction project supply chain. It has been shown that Moderators can "learn" by extracting various kinds of knowledge from Post Project Reports (PPRs) using different types of text mining techniques. Furthermore, it also proposed that the knowledge discovery integrated moderators can be used to support and enhance collaboration by identifying appropriate business opportunities and identifying corresponding partners for creation of a virtual organization. A case study is presented in the context of a UK based SME. Finally, this thesis concludes by summarizing the thesis, outlining its novelties and contributions, and recommending future research

Использование онтологий для построения семантических запросов в реляционных базах данных

На сьогодні всесвітня павутина є найбільшим сховищем інформації. Проте для використання цієї інформації потрібна людина. Мета Семантичного Вебу — представити інформацію у вигляді, придатному для машинної обробки. Він забезпечує можливість спільного доступу до даних, а також їх повторного використання. Велика частина інформації у всесвітній павутині зберігається в реляційних базах даних. Семантичний Веб не може їх використовувати безпосередньо, але реляційні бази даних можуть бути використані для побудови онтологій. Ця ідея привернула увагу багатьох дослідників, які запропонували алгоритми та відповідні програмні рішення для автоматичного або напівавтоматичного вилучення структурованої синтаксичної інформації. У цій роботі досліджено існуючі рішення, показано різні підходи до формалізації логічної моделі реляційної бази даних і перетворення цієї моделі в OWL (мова Семантичного Вебу). Відзначено проблеми розглянутих рішень, а також виділено аспекти, які необхідно враховувати в майбутньому.Nowadays, the Web is the biggest existing information repository. However, to operate with its information human action is required, but the Semantic Web aims to change this. It provides a common framework that allows data to be shared and reused across application, allowing more uses than the traditional Web. Most of the information on the Web is stored in relational databases and the Semantic Web cannot use such databases. Relational databases can be used to construct ontology as the core of the Semantic Web. This task has attracted the interest of many researches, which have made algorithms (wrappers) able to extract structured syntactic information in an automatic or semi-automatic way. At our work we drew experience from those works. We showed different approaches of formalization of a logic model of relational databases, and a transformation of that model into OWL, a Semantic Web language. We closed this paper by mentioning some problems that have only been lightly touched by database to ontology mapping solutions as well as some aspects that need to be considered by future approaches.На сегодняшний день всемирная паутина является крупнейшим хранилищем информации. Тем не менее для использования этой информации необходим человек. Цель Семантического Веба — представить информацию в виде пригодном для машинной обработки. Он обеспечивает возможность совместного доступа к данным, а также их повторного использования. Большая часть информации во всемирной паутине хранится в реляционных базах данных. Семантический Веб не может их использовать непосредственно, но реляционные базы данных могут быть применены для построения онтологий. Эта идея привлекла интерес многих исследователей, которые предложили алгоритмы и соответствующие программные решения для автоматического или полуавтоматического извлечения структурированной синтаксической информации. В этой работе исследованы существующие решения, показаны различные подходы к формализации логической модели реляционной базы данных и преобразования этой модели в OWL (язык Семантического Веба). Отмечены проблемы рассмотренных решений, а также выделены аспекты, которые необходимо учитывать в будущем

DDEC: Dragon database of genes implicated in esophageal cancer

<p>Abstract</p> <p>Background</p> <p>Esophageal cancer ranks eighth in order of cancer occurrence. Its lethality primarily stems from inability to detect the disease during the early organ-confined stage and the lack of effective therapies for advanced-stage disease. Moreover, the understanding of molecular processes involved in esophageal cancer is not complete, hampering the development of efficient diagnostics and therapy. Efforts made by the scientific community to improve the survival rate of esophageal cancer have resulted in a wealth of scattered information that is difficult to find and not easily amendable to data-mining. To reduce this gap and to complement available cancer related bioinformatic resources, we have developed a comprehensive database (Dragon Database of Genes Implicated in Esophageal Cancer) with esophageal cancer related information, as an integrated knowledge database aimed at representing a gateway to esophageal cancer related data.</p> <p>Description</p> <p>Manually curated 529 genes differentially expressed in EC are contained in the database. We extracted and analyzed the promoter regions of these genes and complemented gene-related information with transcription factors that potentially control them. We further, precompiled text-mined and data-mined reports about each of these genes to allow for easy exploration of information about associations of EC-implicated genes with other human genes and proteins, metabolites and enzymes, toxins, chemicals with pharmacological effects, disease concepts and human anatomy. The resulting database, DDEC, has a useful feature to display potential associations that are rarely reported and thus difficult to identify. Moreover, DDEC enables inspection of potentially new 'association hypotheses' generated based on the precompiled reports.</p> <p>Conclusion</p> <p>We hope that this resource will serve as a useful complement to the existing public resources and as a good starting point for researchers and physicians interested in EC genetics. DDEC is freely accessible to academic and non-profit users at <url>http://apps.sanbi.ac.za/ddec/</url>. DDEC will be updated twice a year.</p

The Drosophila anatomy ontology.

BACKGROUND: Anatomy ontologies are query-able classifications of anatomical structures. They provide a widely-used means for standardising the annotation of phenotypes and expression in both human-readable and programmatically accessible forms. They are also frequently used to group annotations in biologically meaningful ways. Accurate annotation requires clear textual definitions for terms, ideally accompanied by images. Accurate grouping and fruitful programmatic usage requires high-quality formal definitions that can be used to automate classification and check for errors. The Drosophila anatomy ontology (DAO) consists of over 8000 classes with broad coverage of Drosophila anatomy. It has been used extensively for annotation by a range of resources, but until recently it was poorly formalised and had few textual definitions. RESULTS: We have transformed the DAO into an ontology rich in formal and textual definitions in which the majority of classifications are automated and extensive error checking ensures quality. Here we present an overview of the content of the DAO, the patterns used in its formalisation, and the various uses it has been put to. CONCLUSIONS: As a result of the work described here, the DAO provides a high-quality, queryable reference for the wild-type anatomy of Drosophila melanogaster and a set of terms to annotate data related to that anatomy. Extensive, well referenced textual definitions make it both a reliable and useful reference and ensure accurate use in annotation. Wide use of formal axioms allows a large proportion of classification to be automated and the use of consistency checking to eliminate errors. This increased formalisation has resulted in significant improvements to the completeness and accuracy of classification. The broad use of both formal and informal definitions make further development of the ontology sustainable and scalable. The patterns of formalisation used in the DAO are likely to be useful to developers of other anatomy ontologies

DYNAMO-MAS: a multi-agent system for ontology evolution from text

International audienceManual ontology development and evolution are complex and time-consuming tasks, even when textual documents are used as knowledge sources in addition to human expertise or existing ontologies. Processing natural language in text produces huge amounts of linguistic data that need to be filtered out and structured. To support both of these tasks, we have developed DYNAMO-MAS, an interactive tool based on an adaptive multi-agent system (adaptive MAS or AMAS) that builds and evolves ontologies from text. DYNA-MO-MAS is a partner system to build ontologies; the ontologist interacts with the system to validate or modify its outputs. This paper presents the architecture of DYNAMO-MAS, its operating principles and its evaluation on three case studies

Design of an E-learning system using semantic information and cloud computing technologies

Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí